求高手迅速英语翻译成中文when the robot senses, and when it moves, respectively. Suppose the robot just sensed s. Markov localization then P (l j s) = ff P(s j l) P(l)where ff is a normalizer that ensures that the resulting pr

求高手迅速英语翻译成中文when the robot senses, and when it moves, respectively. Suppose the robot just sensed s. Markov localization then P (l j s) = ff P(s j l) P(l)where ff is a normalizer that ensures that the resulting pr
求高手迅速英语翻译成中文
when the robot senses, and when it moves, respectively.
Suppose the robot just sensed s. Markov localization then
P (l j s) = ff P(s j l) P(l)
where ff is a normalizer that ensures that the resulting prob-
abilities sum up to one. When the robot moves, Markov
localization updates P(l)
ability:
P ( 0l ) =
using the Theorem of total prob-
Z
P ( 0l j a;l) P(l) dl
Here a denotes an action command. These two update
equations form the basis of Markov localization. Strictly
speaking, they are only applicable if the environment meets
a conditional independence assumption (Markov assump-
tion), which specifies that the robot's pose is the only state
therein. Put differently, Markov localization applies only to
static environments.
Unfortunately, the standard Markov localization ap-
proach is prone to fail in densely populated environments,
since those violate the underlying Markov assumption. In
the museum, people frequently blocked the robot's sensors,
as illustrated in Figure 1. Figuratively speaking, if people
line up as a "wall" in front of the robot—which they often
did—, the basic Markov localization paradigm makes the
robot eventually believe that it is indeed in front of a wall.
To remedy this problem, RHINO employs an "entropy
filter" (Fox et al. 1998b). This filter, which is applied to all
proximity measurements individually, sorts measurements
into two buckets: one that is assumed to contain all cor-
rupted sensor readings, and one that is assumed to contain
only authentic (non-corrupted) ones. To determine which
sensor reading is corrupted and which one is not, the en-
tropy filter measures the relative entropy of the belief state
before and after incorporating a proximity measurement:
P(l) logP(l) dl + P(l j s)logP(l j s) dl
l Sensor readings that increase the robot's certainty
(_H(l;s) > 0 ) are assumed to be authentic. All other sen-
sor readings are assumed to be corrupted and are therefore
not incorporated into the robot's belief. In the museum,
certainty filters reliably identified sensor readings that were
corrupted by the presence of people, as long as the robot
knew its approximate pose. Unfortunately, the entropy fil-
ter can prevent recovery once the robot looses its position
entirely. To prevent this problem, our approach also incor-
porates a small number of randomly chosen sensor readings
in addition to those selected by the entropy filter. See (Fox
et al. 1998b) for an alternative solution to this problem.

求高手迅速英语翻译成中文when the robot senses, and when it moves, respectively. Suppose the robot just sensed s. Markov localization then P (l j s) = ff P(s j l) P(l)where ff is a normalizer that ensures that the resulting pr
当机器人的感觉,当它移动时,分别为.
假设机器人只是感觉到秒马尔可夫定位,然后
P（升Ĵ s）为FF p上（的J升）芘（升）
其中FF是一个正规化,确保由此产生的概率
能力总结为一个.当机器人的动作,马尔可夫
本地化更新P（升）
能力：
P（：01）=
使用的总概率定理,
ž
P（〇升Ĵ了;升）芘（升）分升
这里指的行动命令.这两个更新
方程的形式对马尔可夫定位的基础.严格
而言,他们是只适用的环境符合
条件独立性假设（马尔可夫假定：-
tion）,其中规定,该机器人的构成是唯一的国家
其中.换句话说,马尔可夫定位只适用于
静态环境.
不幸的是,标准马尔可夫定位的AP -
proach容易失败在人口稠密的环境中,
因为这些违反基本马尔可夫假设.在
博物馆里,人们经常堵住了机器人的传感器,
如图1所示.形象地说,如果人们
排队为“墙”在机器人的前面,他们往往
确实,基本马尔可夫定位模式,使
机器人终于相信它确实是在一墙前.
为了解决这个问题,犀牛采用了“熵
过滤器“（福克斯等人.1998年b）.此过滤器,它是适用于所有
个别接近测量,各种测量
两个水桶：一个包含所有被假定为肺心病,
rupted传感器的读数,而且是假设包含
只有真实的（非损坏）的.要确定哪些
传感器的读数已损坏,这主要是因为,恩,
熵的信念状态相对熵过滤措施
前后装有感应测量：
P（L）的疏水常数（升）升+ P（升Ĵ s）疏水常数（升Ĵ s）分升
升传感器的读数,增加机器人的确定性
（_H（升中,S）“0）被认为是真实的.所有其他森
长远发展策略的读数被认为是损坏的,因此
没有纳入机器人的信念.在博物馆里,
可靠地确定过滤器传感器读数被发现
败坏了在场的人,只要机器人
知道它的大致构成.不幸的是,熵过滤,
之三可以防止机器人一旦恢复其立场松动
完全.为了避免这个问题,我们的做法也incor -
porates一个随机选择的传感器读数少数
除了由选定的过滤器的熵.见（福克斯
等.1998年b）对于这个问题的替代解决方案